What I've been teaching

Most "AI agent" lectures start at LangChain. This one starts at a single LLM call and earns every abstraction it adds. Ten chapters, each driven by a real limit that forces the next pattern: from stateless calls → multi-turn → tools → fixed workflows → autonomous tool selection → feedback loops → planning → multi-agent orchestration. The structure is the lesson.

The whole deck is hand-built — reveal.js with a custom warm-ink design system, SVG diagrams with draw-in animations, chapter-tinted accents. No theme bought, no template borrowed. Open-sourced under MIT so others can fork it and adapt it for their own training.

A two-day intensive built for engineers who write firmware, run fabs, design chips — people who'd never touched an LLM and didn't need to become Python developers, but who'd be using AI agents in their workflow within a year. The curriculum bridges that gap. Less code than the GenAI Agent course; more on building intuition about what these systems can and cannot do.

Training infrastructure I also built end-to-end: a React slide SPA, a FastAPI server doing OIDC-SSO into the corporate identity, and an automated LLM-grading pipeline for the exercises. Full curriculum open-sourced under MIT.

A one-shot seminar for an audience that didn't write code. The question on the table: AI is supposedly replacing developers — is it? And if not, what is it actually doing? I made the case for vibe coding as the most visible productivity pattern AI has produced so far, and what it does and doesn't change about who is needed.

Slides are HTML, designed as an editorial keynote rather than a tech deck. Built in the same hand-rolled style as the GenAI Agent course — no framework theme.